Social Media Mining with R
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- Learn how to face the challenges of analyzing social media data
- Get hands-on experience with the most common, up-to-date sentiment analysis tools and apply them to data collected from social media websites through a series of in-depth case studies, which includes how to mine Twitter data
- A focused guide to help you achieve practical results when interpreting social media data
Table of Contents
Chapter 1: Going Viral
Chapter 2: Getting Started with R
Chapter 3: Mining Twitter with R
Chapter 4: Potentials and Pitfalls of Social Media Data
Chapter 5: Social Media Mining – Fundamentals
Chapter 6: Social Media Mining – Case Studies
Appendix: Conclusions and Next Steps
Download the code and support files for this book.
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What you will learn from this book
- Learn the basics of R and all the data types
- Explore the vast expanse of social science research
- Discover more about data potential, the pitfalls, and inferential gotchas
- Gain an insight into the concepts of supervised and unsupervised learning
- Familiarize yourself with visualization and some cognitive pitfalls
- Delve into exploratory data analysis
- Understand the minute details of sentiment analysis
The growth of social media over the last decade has revolutionized the way individuals interact and industries conduct business. Individuals produce data at an unprecedented rate by interacting, sharing, and consuming content through social media. However, analyzing this ever-growing pile of data is quite tricky and, if done erroneously, could lead to wrong inferences.
By using this essential guide, you will gain hands-on experience with generating insights from social media data. This book provides detailed instructions on how to obtain, process, and analyze a variety of socially-generated data while providing a theoretical background to help you accurately interpret your findings. You will be shown R code and examples of data that can be used as a springboard as you get the chance to undertake your own analyses of business, social, or political data.
The book begins by introducing you to the topic of social media data, including its sources and properties. It then explains the basics of R programming in a straightforward, unassuming way. Thereafter, you will be made aware of the inferential dangers associated with social media data and how to avoid them, before describing and implementing a suite of social media mining techniques.
Social Media Mining in R provides a light theoretical background, comprehensive instruction, and state-of-the-art techniques, and by reading this book, you will be well equipped to embark on your own analyses of social media data.
A concise, hands-on guide with many practical examples and a detailed treatise on inference and social science research that will help you in mining data in the real world.
Who this book is for
Whether you are an undergraduate who wishes to get hands-on experience working with social data from the Web, a practitioner wishing to expand your competencies and learn unsupervised sentiment analysis, or you are simply interested in social data analysis, this book will prove to be an essential asset. No previous experience with R or statistics is required, though having knowledge of both will enrich your experience.